Compression is used in many imaging applications, including digital cameras, broadcast TV and DVDs, to increase the number of images that can be stored in a memory or to reduce the transmission bandwidth. If the compression ratio is high, then visible artifacts can result in the decompressed images due to quantization and coefficient truncation side effects. A practical solution filters the decompressed image to suppress the visible artifacts and to guarantee a subjective quality of the decompressed images.
Most video coding standards such as ITU-T H.26× and MPEG-1/2/4 use a block-based process. At high compression ratios, a number of artifacts are visible due to the underlying block-based processing. The most common artifacts are blocking and ringing.
The blocking artifacts appear as grid noise along block boundaries in monotone areas of a decompressed image. Blocking artifacts occur because adjacent blocks are processed independently so that pixels intensities at block boundaries do not line up perfectly after decompression. The ringing artifacts are more pronounced along edges of the decompressed image. This effect, known as Gibb's phenomenon, is caused by truncation of high-frequency coefficients due to quantization AC coefficients.
Many post-processing techniques are known for removing these coding artifacts.
Spatial domain methods are described in U.S. Pat. No. 6,539,060, “Image data post-processing method for reducing quantization effect, apparatus therefor,” issued to Lee et al. on Mar. 25, 2003, U.S. Pat. No. 6,496,605, “Block deformation removing filter, image processing apparatus using the same, method of filtering image signal, and storage medium for storing software therefor,” issued to Osa on Dec. 17, 2002, U.S. Pat. No. 6,320,905, “Postprocessing system for removing blocking artifacts in block-based codecs,” issued to Konstantinides on Nov. 20, 2001, U.S. Pat. No. 6,178,205, “Video postfiltering with motion-compensated temporal filtering and/or spatial-adaptive filtering,” issued to Cheung et al. on Jan. 23, 2001, U.S. Pat. No. 6,167,157, “Method of reducing quantization noise generated during a decoding process of image data and device for decoding image data,” issued to Sugahara et al. on Dec. 26, 2000, and U.S. Pat. No. 5,920,356, “Coding parameter adaptive transform artifact reduction process,” issued to Gupta et al. on Jul. 6, 1999.
Discrete cosine transform (DCT) domain methods are described by Triantafyllidis, et al., “Blocking artifact detection and reduction in compressed data,” IEEE Transactions on Circuits and Systems for Video Technology, Vol. 12, October 2002, and Chen, et al., “Adaptive post-filtering of transform coefficients for the reduction of blocking artifacts,” IEEE Transactions on Circuits and Systems for Video Technology, Vol. 11, May 2001.
Wavelet-based filtering methods are described by Xiong, et al., “A deblocking algorithm for JPEG compressed images using overcomplete wavelet representations,” IEEE Transactions on Circuits and Systems for Video Technology, Vol. 7, No. 2, August 1997, and Lang, et al., “Noise reduction using an undecimated discrete wavelet transform,” Signal Processing Newsletters, Vol. 13, January 1996.
Iterative methods are described by Paek, et al., “A DCT-based spatially adaptive post-processing technique to reduce the blocking artifacts in transform coded images,” IEEE Transactions on Circuits and Systems for Video Technology, Vol. 10, February 2000, and Paek, et al., “On the POCS-based post-processing technique to reduce the blocking artifacts in transform coded images,” IEEE Transactions on Circuits and Systems for Video Technology, Vol. 8, June 1998.
Fuzzy rule-based filtering methods are described by Arakawa, “Fuzzy rule-based signal processing and its application to image restoration,” IEEE Journal on selected areas in communications, Vol. 12, No. 9, December 1994, and U.S. Pat. No. 6,332,136, “Fuzzy filtering method and associated fuzzy filter,” issued to Giura et al. on Dec. 18, 2001.
Most of the prior art methods deal only with blocking noise. Those methods are not effective for ringing noise. Some methods, such as the wavelet-based methods, can suppress ringing, but cause blurring in the decompressed entire image. The prior art of fuzzy rule-based filtering method deals only with white Gaussian noise.
The above prior art methods operate individually on pixels, and apply the same filter to each pixel. Those methods do not consider the underlying content of the image, as a whole. Therefore, those filters either smooth the image excessively to eliminate the artifacts, which causes blurring, or cannot reduce the artifacts sufficiently if minimal smoothing is applied.
Another problem of those methods is the computational complexity. For example, the wavelet-based method requires eight convolution-based low-pass and high-pass filtering operations to obtain the wavelet images. Then, the de-blocking operation is performed on these wavelet images to remove the blocking artifacts. To reconstruct the de-blocked image, twelve convolution-based low-pass and high-pass filtering operations are required. Thus, a total of twenty convolution-based filtering operations are required in that method. The computational cost cannot meet the requirements of real-time processing. Similar to the wavelet-based method, DCT-domain method also has high computational complexity. For low-pass filtering using a 5×5 window, twenty-five DCT operations are required for processing a single 8×8 block. Such high complexity is also impractical for real-time processing. The computational cost for the iterative method is even higher than that of the above two methods. As for the fuzzy rule-based filtering method, the iterative method requires a large number of filter parameters, and extra training data.
In view of the problems of the prior art methods, it is desired to provide a new filtering mechanism that achieves better image/video quality with a low computational complexity.